library(bigMap)

Load data

load('./P100K.RData')

Load results

load('./umap.RData')
load('./fitsne.RData')
load('./ptsne.RData')
ls()
## [1] "fitsne.list" "P100K"       "ptsne.list"  "umap.list"

Output

UMAP

FIt-SNE

pt-SNE

hl-Correlation

pt-SNE

hlTable <- t(sapply(ptsne.list, function(m) summary(m$hlC)))
rownames(hlTable) <- sapply(ptsne.list, function(m) m$ppx$ppx)
knitr::kable(hlTable, caption = 'hl-Correlation') %>%
  kable_styling(full_width = F)
hl-Correlation
Min. 1st Qu. Median Mean 3rd Qu. Max.
50 0.0752411 0.0807705 0.0851543 0.0846177 0.0870846 0.0970239
500 0.3387809 0.3436912 0.3452630 0.3458974 0.3493087 0.3521840
5000 0.4628443 0.4711274 0.4755687 0.4749658 0.4795854 0.4847401
10000 0.4190242 0.4300974 0.4405758 0.4380868 0.4466536 0.4528383

K-ary neighborhood preservation

bdm.knp.plot(umap.list, ppxfrmt = 0)
bdm.knp.plot(fitsne.list, ppxfrmt = 0)
bdm.knp.plot(ptsne.list, ppxfrmt = 0)

Running Times

rTimes <- t(sapply(ptsne.list, function(m) (m$ppx$t[3] +m$t$ptsne[3])))
rTimes <- round(rTimes /60, 2)
knitr::kable(rTimes, caption = 'Computation times (min)') %>%
  kable_styling(full_width = F)
Computation times (min)
elapsed elapsed elapsed elapsed
55.24 52.33 71.29 100.92

Run on: Intel(R) Xeon(R) CPU E5-2650 v3 2.30GHz, 32Mb cache, 41 cores, 4GB/core RAM.